ABoVE: Angular-corrected MODIS MAIAC Reflectance across Alaska and Canada, 2000-2017

简介

该数据集提供了 2000 年至 2017 年阿拉斯加和加拿大西部 ABoVE 区域 MODIS 多角度大气校正算法 (MAIAC) 表面反射率的角度校正。使用机器学习方法随机森林 (RF)，将原始 MAIAC 反射率数据校正为一致的视角和照明角度（0 度视角天顶角和 45 度太阳天顶角），以减少由于角度效应造成的伪影和变化。保留了原始 MAIAC 数据的亚日时间分辨率和 1 公里空间分辨率，包括 7 个陆地波段（波段 1-7）和 5 个海洋波段（波段 8-12）。由此产生的表面反射率数据适用于对表面现象的模式、过程和动态进行长期研究。数据涵盖 11 个不同的 Terra 和 Aqua 卫星 MODIS MAIAC 图块。

摘要

Bits

Definition

0–2

Cloud Mask

000 = Undefined; 001 = Clear; 010 = Possibly Cloudy (detected by AOT filter);

011 = Cloudy (detected by cloud mask algorithm); 101 = Cloud Shadow;

110 = Hot spot of fire; 111 = Water Sediments

3–4

Land Water Snow/Ice Mask

00 = Land; 01 = Water; 10 = Snow; 11 = Ice

5–7

Adjacency Mask

000 = Normal condition; 001 = Adjacent to cloud;

010 = Surrounded by more than 8 cloudy pixels; 011 = Single cloudy pixel;

100 = Adjacent to snow; 101 = Snow was previously detected on this pixel

8

Aerosol Optical Thickness (AOT) Level

0 = AOT is low (<=0.6); 1 = AOT is high (> 0.6) or undefined

9

Algorithm Initialize Status

0 = Initialized; 1 = Not initialized

10

BRF Retrieved Over Snow (use AOT=0.05)

0 = no; 1 = yes

11

Altitude >3.5km (BRF is retrieved with AOT=0.01)

0 = no; 1 = yes

ABoVE：2000-2017 年阿拉斯加和加拿大 MODIS MAIAC 角度校正反射率_#数据挖掘

代码

!pip install leafmap
!pip install pandas
!pip install folium
!pip install matplotlib
!pip install mapclassifyimport pandas as pd
import leafmapurl = "https://github.com/opengeos/NASA-Earth-Data"
df = pd.read_csv(url, sep="\t")
dfleafmap.nasa_data_login()results, gdf = leafmap.nasa_data_search(short_name="ABoVE_MODIS_MAIAC_Reflectance_1858",cloud_hosted=True,bounding_box=(-180.0, 44.12, 180.0, 80.81),temporal=("2000-02-24", "2017-12-31"),count=-1,  # use -1 to return all datasetsreturn_gdf=True,
)gdf.explore()#leafmap.nasa_data_download(results[:5], out_dir="data")